xMemory: Why Top-k Retrieval Breaks for Agent Memory

Traditional similarity-based memory retrieval fails for long-running AI agents because conversation histories contain redundant, interconnected information that causes top-k search to collapse into near-duplicates while missing crucial context. xMemory solves this by restructuring chat history into a four-layer hierarchy of messages, episodes, semantic components, and themes, then retrieving information top-down to maximize coverage without redundancy. Testing shows 21% improvement in accuracy while reducing token usage by 28%, demonstrating that agent memory requires timeline reconstruction rather than document-style search.